{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# pandas 进阶修炼 ｜早起Python\n",
    "<br>\n",
    "\n",
    "**本习题由公众号【早起Python & 可视化图鉴】 原创，转载及其他形式合作请与我们联系（微信号`sshs321`)，未经授权严禁搬运及二次创作，侵权必究！**\n",
    "\n",
    "\n",
    "\n",
    "本习题基于 `pandas` 版本 `1.1.3`，所有内容应当在 `Jupyter Notebook` 中执行以获得最佳效果。\n",
    "\n",
    "不同版本之间写法可能会有少许不同，如若碰到此情况，你应该学会如何自行检索解决。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4 - 数据统计描述性分析\n",
    "\n",
    "\n",
    "<br>\n",
    "\n",
    "\n",
    "在上一章完成基本的数据预览以及缺失值和重复值的处理后。\n",
    "\n",
    "下一个步骤就是对数据进行简单的统计描述性分析，进一步观察数据特征。\n",
    "\n",
    "本章就整理了部分常见操作进行练习。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 初始化\n",
    "\n",
    "<br>\n",
    "\n",
    "该 `Notebook` 版本为**纯习题版**\n",
    "\n",
    "如果需要答案或者提示，可以微信搜索公众号「早起Python」获取！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 加载数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_excel(\"2020年中国大学排名.xlsx\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据探索"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1 - 查看数据\n",
    "\n",
    "<br>\n",
    "\n",
    "查看数据前 10 行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "index",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "排名",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "学校名称",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "省市",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "学校类型",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "总分",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "办学层次得分",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "学科水平得分",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "办学资源得分",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "师资规模与结构得分",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "人才培养得分",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "科学研究得分",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "社会服务得分",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "高端人才得分",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "重大项目与成果得分",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "国际竞争力得分",
         "rawType": "float64",
         "type": "float"
        }
       ],
       "ref": "c379646f-0179-480c-aa7c-b1e607368f4d",
       "rows": [
        [
         "0",
         "1",
         "清华大学",
         "北京",
         "综合",
         "852.5",
         "38.2",
         "72.4",
         "39.6",
         "48.4",
         "256.8",
         "69.1",
         "40.6",
         "76.5",
         "131.0",
         "79.9"
        ],
        [
         "1",
         "2",
         "北京大学",
         "北京",
         "综合",
         "746.7",
         "36.1",
         "73.1",
         "24.6",
         "49.2",
         "237.6",
         "71.0",
         "16.2",
         "71.9",
         "105.8",
         "61.2"
        ],
        [
         "2",
         "3",
         "浙江大学",
         "浙江",
         "综合",
         "649.2",
         "33.9",
         "65.3",
         "20.1",
         "48.3",
         "215.3",
         "68.6",
         "23.9",
         "49.1",
         "81.7",
         "43.0"
        ],
        [
         "3",
         "4",
         "上海交通大学",
         "上海",
         "综合",
         "625.9",
         "35.4",
         "53.6",
         "22.1",
         "43.8",
         "192.8",
         "81.2",
         "18.1",
         "45.8",
         "93.0",
         "40.1"
        ],
        [
         "4",
         "5",
         "南京大学",
         "江苏",
         "综合",
         "566.1",
         "35.1",
         "47.8",
         "10.3",
         "47.4",
         "218.6",
         "59.6",
         "5.3",
         "42.0",
         "71.2",
         "29.0"
        ],
        [
         "5",
         "6",
         "复旦大学",
         "上海",
         "综合",
         "556.7",
         "36.6",
         "48.4",
         "14.9",
         "46.3",
         "198.5",
         "65.7",
         "6.5",
         "42.9",
         "62.0",
         "34.8"
        ],
        [
         "6",
         "7",
         "中国科学技术大学",
         "安徽",
         "理工",
         "526.4",
         "40.0",
         "39.1",
         "10.6",
         "45.9",
         "191.5",
         "52.6",
         "0.2",
         "55.1",
         "49.2",
         "42.2"
        ],
        [
         "7",
         "8",
         "华中科技大学",
         "湖北",
         "综合",
         "497.7",
         "31.9",
         "45.2",
         "11.3",
         "44.2",
         "182.8",
         "58.3",
         "22.0",
         "25.5",
         "44.9",
         "31.8"
        ],
        [
         "8",
         "9",
         "武汉大学",
         "湖北",
         "综合",
         "488.0",
         "31.7",
         "48.4",
         "9.9",
         "45.3",
         "198.8",
         "51.3",
         "11.8",
         "21.4",
         "44.2",
         "25.2"
        ],
        [
         "9",
         "10",
         "中山大学",
         "广东",
         "综合",
         "457.2",
         "30.3",
         "47.1",
         "13.7",
         "46.8",
         "154.4",
         "65.9",
         "5.6",
         "27.1",
         "33.8",
         "32.6"
        ]
       ],
       "shape": {
        "columns": 15,
        "rows": 10
       }
      },
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>排名</th>\n",
       "      <th>学校名称</th>\n",
       "      <th>省市</th>\n",
       "      <th>学校类型</th>\n",
       "      <th>总分</th>\n",
       "      <th>办学层次得分</th>\n",
       "      <th>学科水平得分</th>\n",
       "      <th>办学资源得分</th>\n",
       "      <th>师资规模与结构得分</th>\n",
       "      <th>人才培养得分</th>\n",
       "      <th>科学研究得分</th>\n",
       "      <th>社会服务得分</th>\n",
       "      <th>高端人才得分</th>\n",
       "      <th>重大项目与成果得分</th>\n",
       "      <th>国际竞争力得分</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>清华大学</td>\n",
       "      <td>北京</td>\n",
       "      <td>综合</td>\n",
       "      <td>852.5</td>\n",
       "      <td>38.2</td>\n",
       "      <td>72.4</td>\n",
       "      <td>39.6</td>\n",
       "      <td>48.4</td>\n",
       "      <td>256.8</td>\n",
       "      <td>69.1</td>\n",
       "      <td>40.6</td>\n",
       "      <td>76.5</td>\n",
       "      <td>131.0</td>\n",
       "      <td>79.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>北京大学</td>\n",
       "      <td>北京</td>\n",
       "      <td>综合</td>\n",
       "      <td>746.7</td>\n",
       "      <td>36.1</td>\n",
       "      <td>73.1</td>\n",
       "      <td>24.6</td>\n",
       "      <td>49.2</td>\n",
       "      <td>237.6</td>\n",
       "      <td>71.0</td>\n",
       "      <td>16.2</td>\n",
       "      <td>71.9</td>\n",
       "      <td>105.8</td>\n",
       "      <td>61.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>浙江大学</td>\n",
       "      <td>浙江</td>\n",
       "      <td>综合</td>\n",
       "      <td>649.2</td>\n",
       "      <td>33.9</td>\n",
       "      <td>65.3</td>\n",
       "      <td>20.1</td>\n",
       "      <td>48.3</td>\n",
       "      <td>215.3</td>\n",
       "      <td>68.6</td>\n",
       "      <td>23.9</td>\n",
       "      <td>49.1</td>\n",
       "      <td>81.7</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>上海交通大学</td>\n",
       "      <td>上海</td>\n",
       "      <td>综合</td>\n",
       "      <td>625.9</td>\n",
       "      <td>35.4</td>\n",
       "      <td>53.6</td>\n",
       "      <td>22.1</td>\n",
       "      <td>43.8</td>\n",
       "      <td>192.8</td>\n",
       "      <td>81.2</td>\n",
       "      <td>18.1</td>\n",
       "      <td>45.8</td>\n",
       "      <td>93.0</td>\n",
       "      <td>40.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>南京大学</td>\n",
       "      <td>江苏</td>\n",
       "      <td>综合</td>\n",
       "      <td>566.1</td>\n",
       "      <td>35.1</td>\n",
       "      <td>47.8</td>\n",
       "      <td>10.3</td>\n",
       "      <td>47.4</td>\n",
       "      <td>218.6</td>\n",
       "      <td>59.6</td>\n",
       "      <td>5.3</td>\n",
       "      <td>42.0</td>\n",
       "      <td>71.2</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>复旦大学</td>\n",
       "      <td>上海</td>\n",
       "      <td>综合</td>\n",
       "      <td>556.7</td>\n",
       "      <td>36.6</td>\n",
       "      <td>48.4</td>\n",
       "      <td>14.9</td>\n",
       "      <td>46.3</td>\n",
       "      <td>198.5</td>\n",
       "      <td>65.7</td>\n",
       "      <td>6.5</td>\n",
       "      <td>42.9</td>\n",
       "      <td>62.0</td>\n",
       "      <td>34.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>中国科学技术大学</td>\n",
       "      <td>安徽</td>\n",
       "      <td>理工</td>\n",
       "      <td>526.4</td>\n",
       "      <td>40.0</td>\n",
       "      <td>39.1</td>\n",
       "      <td>10.6</td>\n",
       "      <td>45.9</td>\n",
       "      <td>191.5</td>\n",
       "      <td>52.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>55.1</td>\n",
       "      <td>49.2</td>\n",
       "      <td>42.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>华中科技大学</td>\n",
       "      <td>湖北</td>\n",
       "      <td>综合</td>\n",
       "      <td>497.7</td>\n",
       "      <td>31.9</td>\n",
       "      <td>45.2</td>\n",
       "      <td>11.3</td>\n",
       "      <td>44.2</td>\n",
       "      <td>182.8</td>\n",
       "      <td>58.3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>25.5</td>\n",
       "      <td>44.9</td>\n",
       "      <td>31.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>武汉大学</td>\n",
       "      <td>湖北</td>\n",
       "      <td>综合</td>\n",
       "      <td>488.0</td>\n",
       "      <td>31.7</td>\n",
       "      <td>48.4</td>\n",
       "      <td>9.9</td>\n",
       "      <td>45.3</td>\n",
       "      <td>198.8</td>\n",
       "      <td>51.3</td>\n",
       "      <td>11.8</td>\n",
       "      <td>21.4</td>\n",
       "      <td>44.2</td>\n",
       "      <td>25.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>中山大学</td>\n",
       "      <td>广东</td>\n",
       "      <td>综合</td>\n",
       "      <td>457.2</td>\n",
       "      <td>30.3</td>\n",
       "      <td>47.1</td>\n",
       "      <td>13.7</td>\n",
       "      <td>46.8</td>\n",
       "      <td>154.4</td>\n",
       "      <td>65.9</td>\n",
       "      <td>5.6</td>\n",
       "      <td>27.1</td>\n",
       "      <td>33.8</td>\n",
       "      <td>32.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   排名      学校名称  省市 学校类型     总分  办学层次得分  学科水平得分  办学资源得分  师资规模与结构得分  人才培养得分  \\\n",
       "0   1      清华大学  北京   综合  852.5    38.2    72.4    39.6       48.4   256.8   \n",
       "1   2      北京大学  北京   综合  746.7    36.1    73.1    24.6       49.2   237.6   \n",
       "2   3      浙江大学  浙江   综合  649.2    33.9    65.3    20.1       48.3   215.3   \n",
       "3   4    上海交通大学  上海   综合  625.9    35.4    53.6    22.1       43.8   192.8   \n",
       "4   5      南京大学  江苏   综合  566.1    35.1    47.8    10.3       47.4   218.6   \n",
       "5   6      复旦大学  上海   综合  556.7    36.6    48.4    14.9       46.3   198.5   \n",
       "6   7  中国科学技术大学  安徽   理工  526.4    40.0    39.1    10.6       45.9   191.5   \n",
       "7   8    华中科技大学  湖北   综合  497.7    31.9    45.2    11.3       44.2   182.8   \n",
       "8   9      武汉大学  湖北   综合  488.0    31.7    48.4     9.9       45.3   198.8   \n",
       "9  10      中山大学  广东   综合  457.2    30.3    47.1    13.7       46.8   154.4   \n",
       "\n",
       "   科学研究得分  社会服务得分  高端人才得分  重大项目与成果得分  国际竞争力得分  \n",
       "0    69.1    40.6    76.5      131.0     79.9  \n",
       "1    71.0    16.2    71.9      105.8     61.2  \n",
       "2    68.6    23.9    49.1       81.7     43.0  \n",
       "3    81.2    18.1    45.8       93.0     40.1  \n",
       "4    59.6     5.3    42.0       71.2     29.0  \n",
       "5    65.7     6.5    42.9       62.0     34.8  \n",
       "6    52.6     0.2    55.1       49.2     42.2  \n",
       "7    58.3    22.0    25.5       44.9     31.8  \n",
       "8    51.3    11.8    21.4       44.2     25.2  \n",
       "9    65.9     5.6    27.1       33.8     32.6  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2 - 修改索引\n",
    "\n",
    "<br>\n",
    "\n",
    "数据已经按照降序排列，让 学校 当索引会更好一点\n",
    "\n",
    "-> 修改索引为 学校名称 列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "学校名称",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "排名",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "省市",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "学校类型",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "总分",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "办学层次得分",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "学科水平得分",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "办学资源得分",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "师资规模与结构得分",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "人才培养得分",
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         "852.5",
         "38.2",
         "72.4",
         "39.6",
         "48.4",
         "256.8",
         "69.1",
         "40.6",
         "76.5",
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         "24.6",
         "49.2",
         "237.6",
         "71.0",
         "16.2",
         "71.9",
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         "61.2"
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         "3",
         "浙江",
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         "649.2",
         "33.9",
         "65.3",
         "20.1",
         "48.3",
         "215.3",
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         "49.1",
         "81.7",
         "43.0"
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         "625.9",
         "35.4",
         "53.6",
         "22.1",
         "43.8",
         "192.8",
         "81.2",
         "18.1",
         "45.8",
         "93.0",
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         "47.8",
         "10.3",
         "47.4",
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         "59.6",
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         "6",
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         "36.6",
         "48.4",
         "14.9",
         "46.3",
         "198.5",
         "65.7",
         "6.5",
         "42.9",
         "62.0",
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         "526.4",
         "40.0",
         "39.1",
         "10.6",
         "45.9",
         "191.5",
         "52.6",
         "0.2",
         "55.1",
         "49.2",
         "42.2"
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         "497.7",
         "31.9",
         "45.2",
         "11.3",
         "44.2",
         "182.8",
         "58.3",
         "22.0",
         "25.5",
         "44.9",
         "31.8"
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         "9",
         "湖北",
         "综合",
         "488.0",
         "31.7",
         "48.4",
         "9.9",
         "45.3",
         "198.8",
         "51.3",
         "11.8",
         "21.4",
         "44.2",
         "25.2"
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         "10",
         "广东",
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         "457.2",
         "30.3",
         "47.1",
         "13.7",
         "46.8",
         "154.4",
         "65.9",
         "5.6",
         "27.1",
         "33.8",
         "32.6"
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         "452.5",
         "34.3",
         "39.7",
         "9.7",
         "43.7",
         "180.6",
         "44.5",
         "10.2",
         "16.6",
         "48.8",
         "24.5"
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         "12",
         "黑龙江",
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         "450.2",
         "32.7",
         "38.6",
         "5.7",
         "47.2",
         "175.1",
         "39.5",
         "40.2",
         "18.5",
         "25.0",
         "27.8"
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         "13",
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         "445.1",
         "32.8",
         "34.3",
         "12.4",
         "42.7",
         "181.1",
         "32.6",
         "27.0",
         "20.9",
         "36.8",
         "24.6"
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         "14",
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         "440.9",
         "34.8",
         "43.6",
         "11.8",
         "47.6",
         "170.5",
         "39.1",
         "2.7",
         "16.9",
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         "32.0"
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         "15",
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         "439.0",
         "33.4",
         "40.3",
         "11.1",
         "43.5",
         "166.3",
         "47.8",
         "16.4",
         "22.0",
         "36.4",
         "21.8"
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         "16",
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         "435.7",
         "32.5",
         "45.6",
         "8.7",
         "44.7",
         "171.7",
         "46.9",
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         "432.7",
         "33.7",
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         "8.0",
         "44.2",
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         "38.2",
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         "14.1",
         "37.4",
         "28.8"
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         "409.7",
         "34.5",
         "41.7",
         "10.9",
         "46.3",
         "173.4",
         "35.7",
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         "19",
         "天津",
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         "402.1",
         "32.4",
         "34.8",
         "13.8",
         "44.9",
         "166.5",
         "33.5",
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         "28.7",
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         "31.9",
         "30.8",
         "10.7",
         "43.2",
         "159.5",
         "30.1",
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         "20.8"
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         "32.6",
         "33.9",
         "9.6",
         "43.9",
         "156.3",
         "37.8",
         "11.0",
         "17.1",
         "26.7",
         "21.3"
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         "22",
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         "387.9",
         "28.8",
         "40.1",
         "9.0",
         "44.6",
         "162.2",
         "44.4",
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         "12.0",
         "21.2",
         "20.8"
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         "23",
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         "综合",
         "383.3",
         "32.7",
         "37.5",
         "9.3",
         "41.7",
         "159.3",
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         "23.0"
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         "24",
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         "379.5",
         "29.9",
         "42.4",
         "9.1",
         "48.6",
         "157.2",
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         "8.9",
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         "25",
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         "33.2",
         "8.0",
         "42.0",
         "156.7",
         "34.3",
         "9.7",
         "13.4",
         "24.4",
         "27.5"
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         "26",
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         "378.6",
         "30.6",
         "39.1",
         "7.5",
         "42.1",
         "150.4",
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         "6.5",
         "10.3",
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         "23.8"
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         "27",
         "辽宁",
         "理工",
         "365.1",
         "30.1",
         "30.8",
         "6.4",
         "42.9",
         "149.3",
         "33.1",
         "6.1",
         "14.2",
         "33.7",
         "18.5"
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         "28",
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         "359.6",
         "32.5",
         "24.2",
         "10.7",
         "43.6",
         "155.8",
         "29.7",
         "17.7",
         "10.6",
         "14.4",
         "20.4"
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         "29",
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         "358.0",
         "32.6",
         "34.0",
         "8.8",
         "43.2",
         "150.9",
         "31.4",
         "1.8",
         "11.7",
         "25.1",
         "18.5"
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         "30",
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         "351.5",
         "30.9",
         "35.2",
         "8.4",
         "45.0",
         "132.1",
         "26.9",
         "3.2",
         "14.7",
         "37.7",
         "17.3"
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         "31",
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         "348.3",
         "31.5",
         "28.5",
         "5.3",
         "38.2",
         "141.0",
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         "13.4",
         "11.4",
         "24.7",
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         "21.7",
         "7.3",
         "41.7",
         "153.9",
         "23.0",
         "6.2",
         "11.6",
         "7.9",
         "31.1"
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         "321.8",
         "31.4",
         "24.4",
         "5.6",
         "40.2",
         "130.1",
         "26.6",
         "16.6",
         "7.6",
         "21.1",
         "18.3"
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         "34",
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         "320.9",
         "30.2",
         "30.9",
         "7.0",
         "37.5",
         "139.3",
         "29.3",
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         "7.1",
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         "35",
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         "319.5",
         "29.2",
         "21.7",
         "3.7",
         "39.7",
         "150.8",
         "25.1",
         "15.4",
         "8.4",
         "12.1",
         "13.4"
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         "36",
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         "317.1",
         "30.9",
         "24.1",
         "5.6",
         "40.1",
         "146.6",
         "22.0",
         "18.0",
         "5.9",
         "7.7",
         "16.2"
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         "37",
         "辽宁",
         "理工",
         "314.0",
         "31.7",
         "24.2",
         "5.8",
         "37.5",
         "139.1",
         "25.9",
         "13.4",
         "7.1",
         "16.1",
         "13.2"
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         "38",
         "江苏",
         "综合",
         "306.8",
         "29.0",
         "30.4",
         "5.5",
         "39.6",
         "106.3",
         "30.0",
         "9.5",
         "9.2",
         "17.4",
         "30.0"
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         "39",
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         "300.5",
         "27.9",
         "27.0",
         "4.8",
         "36.9",
         "134.2",
         "24.4",
         "1.8",
         "9.9",
         "17.6",
         "16.0"
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         "40",
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         "综合",
         "300.4",
         "30.5",
         "28.8",
         "5.0",
         "37.5",
         "129.1",
         "24.3",
         "1.8",
         "9.2",
         "19.6",
         "14.4"
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         "41",
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         "299.4",
         "29.8",
         "19.7",
         "4.7",
         "37.4",
         "149.4",
         "20.7",
         "4.4",
         "5.7",
         "13.9",
         "13.7"
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         "42",
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         "29.8",
         "19.8",
         "5.5",
         "38.9",
         "127.8",
         "22.4",
         "9.8",
         "10.3",
         "14.7",
         "15.5"
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         "43",
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         "293.6",
         "31.5",
         "26.8",
         "5.2",
         "39.5",
         "129.7",
         "18.8",
         "5.4",
         "4.3",
         "16.5",
         "15.8"
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         "44",
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         "师范",
         "292.4",
         "30.2",
         "26.2",
         "5.1",
         "38.0",
         "133.1",
         "23.4",
         "0.3",
         "3.7",
         "13.3",
         "19.0"
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         "45",
         "上海",
         "综合",
         "290.0",
         "31.2",
         "26.5",
         "6.7",
         "37.9",
         "119.2",
         "22.1",
         "9.1",
         "6.2",
         "15.2",
         "15.9"
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         "46",
         "广东",
         "综合",
         "289.0",
         "26.7",
         "7.1",
         "16.9",
         "41.9",
         "105.0",
         "26.4",
         "1.0",
         "38.9",
         "7.1",
         "18.0"
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         "283.2",
         "29.0",
         "27.5",
         "4.2",
         "36.9",
         "117.2",
         "24.0",
         "2.3",
         "6.4",
         "20.2",
         "15.7"
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         "综合",
         "282.6",
         "27.9",
         "26.1",
         "9.3",
         "39.2",
         "114.8",
         "26.9",
         "1.3",
         "6.1",
         "15.4",
         "15.8"
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         "49",
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         "综合",
         "281.2",
         "30.2",
         "24.6",
         "5.2",
         "38.9",
         "117.9",
         "21.5",
         "2.9",
         "7.3",
         "19.4",
         "13.3"
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         "279.9",
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         "26.5",
         "3.8",
         "38.6",
         "125.8",
         "21.8",
         "3.2",
         "3.0",
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         "14.4"
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       "      <th>学校类型</th>\n",
       "      <th>总分</th>\n",
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       "      <th>清华大学</th>\n",
       "      <td>1</td>\n",
       "      <td>北京</td>\n",
       "      <td>综合</td>\n",
       "      <td>852.5</td>\n",
       "      <td>38.2</td>\n",
       "      <td>72.4</td>\n",
       "      <td>39.6</td>\n",
       "      <td>48.4</td>\n",
       "      <td>256.8</td>\n",
       "      <td>69.1</td>\n",
       "      <td>40.6</td>\n",
       "      <td>76.5</td>\n",
       "      <td>131.0</td>\n",
       "      <td>79.9</td>\n",
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       "    <tr>\n",
       "      <th>北京大学</th>\n",
       "      <td>2</td>\n",
       "      <td>北京</td>\n",
       "      <td>综合</td>\n",
       "      <td>746.7</td>\n",
       "      <td>36.1</td>\n",
       "      <td>73.1</td>\n",
       "      <td>24.6</td>\n",
       "      <td>49.2</td>\n",
       "      <td>237.6</td>\n",
       "      <td>71.0</td>\n",
       "      <td>16.2</td>\n",
       "      <td>71.9</td>\n",
       "      <td>105.8</td>\n",
       "      <td>61.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>浙江大学</th>\n",
       "      <td>3</td>\n",
       "      <td>浙江</td>\n",
       "      <td>综合</td>\n",
       "      <td>649.2</td>\n",
       "      <td>33.9</td>\n",
       "      <td>65.3</td>\n",
       "      <td>20.1</td>\n",
       "      <td>48.3</td>\n",
       "      <td>215.3</td>\n",
       "      <td>68.6</td>\n",
       "      <td>23.9</td>\n",
       "      <td>49.1</td>\n",
       "      <td>81.7</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>上海交通大学</th>\n",
       "      <td>4</td>\n",
       "      <td>上海</td>\n",
       "      <td>综合</td>\n",
       "      <td>625.9</td>\n",
       "      <td>35.4</td>\n",
       "      <td>53.6</td>\n",
       "      <td>22.1</td>\n",
       "      <td>43.8</td>\n",
       "      <td>192.8</td>\n",
       "      <td>81.2</td>\n",
       "      <td>18.1</td>\n",
       "      <td>45.8</td>\n",
       "      <td>93.0</td>\n",
       "      <td>40.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>南京大学</th>\n",
       "      <td>5</td>\n",
       "      <td>江苏</td>\n",
       "      <td>综合</td>\n",
       "      <td>566.1</td>\n",
       "      <td>35.1</td>\n",
       "      <td>47.8</td>\n",
       "      <td>10.3</td>\n",
       "      <td>47.4</td>\n",
       "      <td>218.6</td>\n",
       "      <td>59.6</td>\n",
       "      <td>5.3</td>\n",
       "      <td>42.0</td>\n",
       "      <td>71.2</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>南京邮电大学</th>\n",
       "      <td>96</td>\n",
       "      <td>江苏</td>\n",
       "      <td>综合</td>\n",
       "      <td>213.9</td>\n",
       "      <td>25.0</td>\n",
       "      <td>12.5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>34.8</td>\n",
       "      <td>101.2</td>\n",
       "      <td>12.4</td>\n",
       "      <td>6.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>4.6</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>河南大学</th>\n",
       "      <td>97</td>\n",
       "      <td>河南</td>\n",
       "      <td>综合</td>\n",
       "      <td>212.9</td>\n",
       "      <td>24.2</td>\n",
       "      <td>22.7</td>\n",
       "      <td>3.4</td>\n",
       "      <td>32.5</td>\n",
       "      <td>97.5</td>\n",
       "      <td>15.7</td>\n",
       "      <td>2.1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>4.2</td>\n",
       "      <td>9.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>上海师范大学</th>\n",
       "      <td>98</td>\n",
       "      <td>上海</td>\n",
       "      <td>师范</td>\n",
       "      <td>212.8</td>\n",
       "      <td>27.3</td>\n",
       "      <td>17.9</td>\n",
       "      <td>3.6</td>\n",
       "      <td>32.1</td>\n",
       "      <td>96.9</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>11.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>杭州电子科技大学</th>\n",
       "      <td>99</td>\n",
       "      <td>浙江</td>\n",
       "      <td>理工</td>\n",
       "      <td>211.6</td>\n",
       "      <td>25.4</td>\n",
       "      <td>12.6</td>\n",
       "      <td>2.7</td>\n",
       "      <td>36.5</td>\n",
       "      <td>103.4</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.6</td>\n",
       "      <td>12.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>广州大学</th>\n",
       "      <td>100</td>\n",
       "      <td>广东</td>\n",
       "      <td>综合</td>\n",
       "      <td>211.1</td>\n",
       "      <td>23.2</td>\n",
       "      <td>16.4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>33.7</td>\n",
       "      <td>95.9</td>\n",
       "      <td>14.4</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>14.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           排名  省市 学校类型     总分  办学层次得分  学科水平得分  办学资源得分  师资规模与结构得分  人才培养得分  \\\n",
       "学校名称                                                                       \n",
       "清华大学        1  北京   综合  852.5    38.2    72.4    39.6       48.4   256.8   \n",
       "北京大学        2  北京   综合  746.7    36.1    73.1    24.6       49.2   237.6   \n",
       "浙江大学        3  浙江   综合  649.2    33.9    65.3    20.1       48.3   215.3   \n",
       "上海交通大学      4  上海   综合  625.9    35.4    53.6    22.1       43.8   192.8   \n",
       "南京大学        5  江苏   综合  566.1    35.1    47.8    10.3       47.4   218.6   \n",
       "...       ...  ..  ...    ...     ...     ...     ...        ...     ...   \n",
       "南京邮电大学     96  江苏   综合  213.9    25.0    12.5     2.4       34.8   101.2   \n",
       "河南大学       97  河南   综合  212.9    24.2    22.7     3.4       32.5    97.5   \n",
       "上海师范大学     98  上海   师范  212.8    27.3    17.9     3.6       32.1    96.9   \n",
       "杭州电子科技大学   99  浙江   理工  211.6    25.4    12.6     2.7       36.5   103.4   \n",
       "广州大学      100  广东   综合  211.1    23.2    16.4     5.0       33.7    95.9   \n",
       "\n",
       "          科学研究得分  社会服务得分  高端人才得分  重大项目与成果得分  国际竞争力得分  \n",
       "学校名称                                                  \n",
       "清华大学        69.1    40.6    76.5      131.0     79.9  \n",
       "北京大学        71.0    16.2    71.9      105.8     61.2  \n",
       "浙江大学        68.6    23.9    49.1       81.7     43.0  \n",
       "上海交通大学      81.2    18.1    45.8       93.0     40.1  \n",
       "南京大学        59.6     5.3    42.0       71.2     29.0  \n",
       "...          ...     ...     ...        ...      ...  \n",
       "南京邮电大学      12.4     6.5     1.6        4.6     13.0  \n",
       "河南大学        15.7     2.1     1.3        4.2      9.2  \n",
       "上海师范大学      14.0     0.5     2.0        6.8     11.8  \n",
       "杭州电子科技大学    12.0     2.5     1.5        2.6     12.3  \n",
       "广州大学        14.4     0.6     2.0        5.2     14.8  \n",
       "\n",
       "[100 rows x 14 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index(keys='学校名称')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "核心修改方法\n",
    "\n",
    "| 方法 | 关键功能 | 常用参数说明 | 主要应用场景 |\n",
    "| :--- | :--- | :--- | :--- |\n",
    "| **直接赋值** | 整体替换行/列索引 | `df.index = 新索引列表`<br>`df.columns = 新列名列表` | 需要**完全重置**所有索引标签时 |\n",
    "| **`set_index`** | 将**指定列**设置为新索引 | `keys`：列名（可多列）<br>`drop`：是否保留原列（默认`True`不保留）<br>`inplace`：是否原地修改（默认`False`） | 将数据中的**某一列或多列**（如\"ID\"、\"日期\"）设为主索引 |\n",
    "| **`reset_index`** | 将**索引还原为默认整数索引**，原索引变普通列 | `drop`：是否丢弃原索引（默认`False`，即保留为列）<br>`inplace`：是否原地修改（默认`False`） | 在筛选等操作后**恢复连续的整数索引**，或将索引作为数据列进行分析 |\n",
    "| **`rename`** | **灵活重命名**部分索引标签 | `index`：行索引新旧名映射字典<br>`columns`：列索引新旧名映射字典<br>`inplace`：是否原地修改 | **按需修改个别索引名**，避免整体替换的繁琐 |\n",
    "| **`reindex`** | 按**新给定的索引顺序**重新排列数据，缺失处填充NaN | - | 使数据**与另一个数据集严格对齐**，或**扩展/收缩索引集** |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3 - 查看数据量\n",
    "\n",
    "也就是数据框的 行 * 列，总共单元格的数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1500"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4 - 数据排序\n",
    "\n",
    "<br>\n",
    "\n",
    "将数据按照总分升序排列，并展示前20个学校\n",
    "\n",
    "备注：也就是看倒数20名啦"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "index",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "排名",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "学校名称",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "省市",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "学校类型",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "总分",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "办学层次得分",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "学科水平得分",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "办学资源得分",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "师资规模与结构得分",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "人才培养得分",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "科学研究得分",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "社会服务得分",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "高端人才得分",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "重大项目与成果得分",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "国际竞争力得分",
         "rawType": "float64",
         "type": "float"
        }
       ],
       "ref": "30eb3478-25ea-4d71-976b-b7cd21c2106d",
       "rows": [
        [
         "99",
         "100",
         "广州大学",
         "广东",
         "综合",
         "211.1",
         "23.2",
         "16.4",
         "5.0",
         "33.7",
         "95.9",
         "14.4",
         "0.6",
         "2.0",
         "5.2",
         "14.8"
        ],
        [
         "98",
         "99",
         "杭州电子科技大学",
         "浙江",
         "理工",
         "211.6",
         "25.4",
         "12.6",
         "2.7",
         "36.5",
         "103.4",
         "12.0",
         "2.5",
         "1.5",
         "2.6",
         "12.3"
        ],
        [
         "97",
         "98",
         "上海师范大学",
         "上海",
         "师范",
         "212.8",
         "27.3",
         "17.9",
         "3.6",
         "32.1",
         "96.9",
         "14.0",
         "0.5",
         "2.0",
         "6.8",
         "11.8"
        ],
        [
         "96",
         "97",
         "河南大学",
         "河南",
         "综合",
         "212.9",
         "24.2",
         "22.7",
         "3.4",
         "32.5",
         "97.5",
         "15.7",
         "2.1",
         "1.3",
         "4.2",
         "9.2"
        ],
        [
         "95",
         "96",
         "南京邮电大学",
         "江苏",
         "综合",
         "213.9",
         "25.0",
         "12.5",
         "2.4",
         "34.8",
         "101.2",
         "12.4",
         "6.5",
         "1.6",
         "4.6",
         "13.0"
        ],
        [
         "94",
         "95",
         "广东工业大学",
         "广东",
         "理工",
         "214.2",
         "24.2",
         "15.5",
         "3.7",
         "32.6",
         "96.7",
         "13.8",
         "3.2",
         "3.1",
         "5.3",
         "16.2"
        ],
        [
         "93",
         "94",
         "湖北大学",
         "湖北",
         "综合",
         "214.5",
         "26.3",
         "14.7",
         "2.3",
         "35.0",
         "105.8",
         "10.5",
         "2.9",
         "1.2",
         "3.7",
         "12.1"
        ],
        [
         "92",
         "93",
         "南京信息工程大学",
         "江苏",
         "理工",
         "216.6",
         "23.6",
         "16.1",
         "2.4",
         "33.6",
         "97.5",
         "15.1",
         "4.7",
         "2.1",
         "3.6",
         "18.0"
        ],
        [
         "91",
         "92",
         "燕山大学",
         "河北",
         "理工",
         "216.7",
         "26.6",
         "15.2",
         "2.3",
         "34.5",
         "107.2",
         "12.6",
         "2.8",
         "2.5",
         "4.8",
         "8.2"
        ],
        [
         "90",
         "91",
         "长安大学",
         "陕西",
         "理工",
         "218.9",
         "27.2",
         "14.0",
         "3.7",
         "34.1",
         "104.9",
         "12.1",
         "12.4",
         "1.1",
         "1.1",
         "8.2"
        ],
        [
         "89",
         "90",
         "安徽大学",
         "安徽",
         "综合",
         "219.2",
         "25.7",
         "19.0",
         "2.6",
         "29.5",
         "110.8",
         "15.5",
         "1.4",
         "0.8",
         "3.4",
         "10.5"
        ],
        [
         "88",
         "89",
         "上海理工大学",
         "上海",
         "理工",
         "221.4",
         "28.3",
         "16.4",
         "3.6",
         "32.3",
         "105.7",
         "12.6",
         "9.8",
         "1.3",
         "1.5",
         "9.9"
        ],
        [
         "87",
         "88",
         "华南农业大学",
         "广东",
         "农业",
         "227.1",
         "23.2",
         "18.0",
         "4.5",
         "35.0",
         "102.4",
         "14.8",
         "2.2",
         "4.0",
         "10.3",
         "12.7"
        ],
        [
         "86",
         "87",
         "南昌大学",
         "江西",
         "综合",
         "228.4",
         "27.3",
         "21.1",
         "4.1",
         "32.3",
         "105.2",
         "15.6",
         "0.5",
         "2.5",
         "7.8",
         "12.0"
        ],
        [
         "84",
         "85",
         "湖南师范大学",
         "湖南",
         "师范",
         "233.4",
         "27.9",
         "20.6",
         "2.7",
         "35.2",
         "109.8",
         "15.7",
         "0.5",
         "1.9",
         "7.7",
         "11.4"
        ],
        [
         "85",
         "85",
         "首都师范大学",
         "北京",
         "师范",
         "233.4",
         "29.5",
         "22.9",
         "5.2",
         "37.6",
         "99.7",
         "14.7",
         "0.3",
         "3.3",
         "8.3",
         "11.8"
        ],
        [
         "83",
         "84",
         "云南大学",
         "云南",
         "综合",
         "234.1",
         "29.0",
         "22.8",
         "5.0",
         "30.8",
         "111.6",
         "14.0",
         "0.4",
         "3.0",
         "6.4",
         "11.2"
        ],
        [
         "82",
         "83",
         "郑州大学",
         "河南",
         "综合",
         "234.9",
         "26.3",
         "28.3",
         "6.0",
         "37.2",
         "91.8",
         "23.7",
         "1.7",
         "2.0",
         "4.9",
         "13.0"
        ],
        [
         "81",
         "82",
         "南京工业大学",
         "江苏",
         "理工",
         "235.4",
         "25.4",
         "14.4",
         "3.3",
         "33.0",
         "102.2",
         "15.0",
         "8.6",
         "6.6",
         "12.0",
         "15.1"
        ],
        [
         "80",
         "81",
         "浙江工业大学",
         "浙江",
         "理工",
         "235.5",
         "27.6",
         "18.7",
         "5.7",
         "28.7",
         "109.5",
         "17.8",
         "5.6",
         "2.5",
         "5.8",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>排名</th>\n",
       "      <th>学校名称</th>\n",
       "      <th>省市</th>\n",
       "      <th>学校类型</th>\n",
       "      <th>总分</th>\n",
       "      <th>办学层次得分</th>\n",
       "      <th>学科水平得分</th>\n",
       "      <th>办学资源得分</th>\n",
       "      <th>师资规模与结构得分</th>\n",
       "      <th>人才培养得分</th>\n",
       "      <th>科学研究得分</th>\n",
       "      <th>社会服务得分</th>\n",
       "      <th>高端人才得分</th>\n",
       "      <th>重大项目与成果得分</th>\n",
       "      <th>国际竞争力得分</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>100</td>\n",
       "      <td>广州大学</td>\n",
       "      <td>广东</td>\n",
       "      <td>综合</td>\n",
       "      <td>211.1</td>\n",
       "      <td>23.2</td>\n",
       "      <td>16.4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>33.7</td>\n",
       "      <td>95.9</td>\n",
       "      <td>14.4</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>14.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>99</td>\n",
       "      <td>杭州电子科技大学</td>\n",
       "      <td>浙江</td>\n",
       "      <td>理工</td>\n",
       "      <td>211.6</td>\n",
       "      <td>25.4</td>\n",
       "      <td>12.6</td>\n",
       "      <td>2.7</td>\n",
       "      <td>36.5</td>\n",
       "      <td>103.4</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.6</td>\n",
       "      <td>12.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>98</td>\n",
       "      <td>上海师范大学</td>\n",
       "      <td>上海</td>\n",
       "      <td>师范</td>\n",
       "      <td>212.8</td>\n",
       "      <td>27.3</td>\n",
       "      <td>17.9</td>\n",
       "      <td>3.6</td>\n",
       "      <td>32.1</td>\n",
       "      <td>96.9</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>11.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>97</td>\n",
       "      <td>河南大学</td>\n",
       "      <td>河南</td>\n",
       "      <td>综合</td>\n",
       "      <td>212.9</td>\n",
       "      <td>24.2</td>\n",
       "      <td>22.7</td>\n",
       "      <td>3.4</td>\n",
       "      <td>32.5</td>\n",
       "      <td>97.5</td>\n",
       "      <td>15.7</td>\n",
       "      <td>2.1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>4.2</td>\n",
       "      <td>9.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>96</td>\n",
       "      <td>南京邮电大学</td>\n",
       "      <td>江苏</td>\n",
       "      <td>综合</td>\n",
       "      <td>213.9</td>\n",
       "      <td>25.0</td>\n",
       "      <td>12.5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>34.8</td>\n",
       "      <td>101.2</td>\n",
       "      <td>12.4</td>\n",
       "      <td>6.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>4.6</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>95</td>\n",
       "      <td>广东工业大学</td>\n",
       "      <td>广东</td>\n",
       "      <td>理工</td>\n",
       "      <td>214.2</td>\n",
       "      <td>24.2</td>\n",
       "      <td>15.5</td>\n",
       "      <td>3.7</td>\n",
       "      <td>32.6</td>\n",
       "      <td>96.7</td>\n",
       "      <td>13.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.3</td>\n",
       "      <td>16.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>94</td>\n",
       "      <td>湖北大学</td>\n",
       "      <td>湖北</td>\n",
       "      <td>综合</td>\n",
       "      <td>214.5</td>\n",
       "      <td>26.3</td>\n",
       "      <td>14.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>105.8</td>\n",
       "      <td>10.5</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.2</td>\n",
       "      <td>3.7</td>\n",
       "      <td>12.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>93</td>\n",
       "      <td>南京信息工程大学</td>\n",
       "      <td>江苏</td>\n",
       "      <td>理工</td>\n",
       "      <td>216.6</td>\n",
       "      <td>23.6</td>\n",
       "      <td>16.1</td>\n",
       "      <td>2.4</td>\n",
       "      <td>33.6</td>\n",
       "      <td>97.5</td>\n",
       "      <td>15.1</td>\n",
       "      <td>4.7</td>\n",
       "      <td>2.1</td>\n",
       "      <td>3.6</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>92</td>\n",
       "      <td>燕山大学</td>\n",
       "      <td>河北</td>\n",
       "      <td>理工</td>\n",
       "      <td>216.7</td>\n",
       "      <td>26.6</td>\n",
       "      <td>15.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>34.5</td>\n",
       "      <td>107.2</td>\n",
       "      <td>12.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.8</td>\n",
       "      <td>8.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>91</td>\n",
       "      <td>长安大学</td>\n",
       "      <td>陕西</td>\n",
       "      <td>理工</td>\n",
       "      <td>218.9</td>\n",
       "      <td>27.2</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>34.1</td>\n",
       "      <td>104.9</td>\n",
       "      <td>12.1</td>\n",
       "      <td>12.4</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>8.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>90</td>\n",
       "      <td>安徽大学</td>\n",
       "      <td>安徽</td>\n",
       "      <td>综合</td>\n",
       "      <td>219.2</td>\n",
       "      <td>25.7</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>29.5</td>\n",
       "      <td>110.8</td>\n",
       "      <td>15.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>89</td>\n",
       "      <td>上海理工大学</td>\n",
       "      <td>上海</td>\n",
       "      <td>理工</td>\n",
       "      <td>221.4</td>\n",
       "      <td>28.3</td>\n",
       "      <td>16.4</td>\n",
       "      <td>3.6</td>\n",
       "      <td>32.3</td>\n",
       "      <td>105.7</td>\n",
       "      <td>12.6</td>\n",
       "      <td>9.8</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.5</td>\n",
       "      <td>9.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>88</td>\n",
       "      <td>华南农业大学</td>\n",
       "      <td>广东</td>\n",
       "      <td>农业</td>\n",
       "      <td>227.1</td>\n",
       "      <td>23.2</td>\n",
       "      <td>18.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>35.0</td>\n",
       "      <td>102.4</td>\n",
       "      <td>14.8</td>\n",
       "      <td>2.2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.3</td>\n",
       "      <td>12.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>87</td>\n",
       "      <td>南昌大学</td>\n",
       "      <td>江西</td>\n",
       "      <td>综合</td>\n",
       "      <td>228.4</td>\n",
       "      <td>27.3</td>\n",
       "      <td>21.1</td>\n",
       "      <td>4.1</td>\n",
       "      <td>32.3</td>\n",
       "      <td>105.2</td>\n",
       "      <td>15.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>7.8</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>85</td>\n",
       "      <td>湖南师范大学</td>\n",
       "      <td>湖南</td>\n",
       "      <td>师范</td>\n",
       "      <td>233.4</td>\n",
       "      <td>27.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>2.7</td>\n",
       "      <td>35.2</td>\n",
       "      <td>109.8</td>\n",
       "      <td>15.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.9</td>\n",
       "      <td>7.7</td>\n",
       "      <td>11.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>85</td>\n",
       "      <td>首都师范大学</td>\n",
       "      <td>北京</td>\n",
       "      <td>师范</td>\n",
       "      <td>233.4</td>\n",
       "      <td>29.5</td>\n",
       "      <td>22.9</td>\n",
       "      <td>5.2</td>\n",
       "      <td>37.6</td>\n",
       "      <td>99.7</td>\n",
       "      <td>14.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>8.3</td>\n",
       "      <td>11.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>84</td>\n",
       "      <td>云南大学</td>\n",
       "      <td>云南</td>\n",
       "      <td>综合</td>\n",
       "      <td>234.1</td>\n",
       "      <td>29.0</td>\n",
       "      <td>22.8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>30.8</td>\n",
       "      <td>111.6</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.4</td>\n",
       "      <td>11.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>83</td>\n",
       "      <td>郑州大学</td>\n",
       "      <td>河南</td>\n",
       "      <td>综合</td>\n",
       "      <td>234.9</td>\n",
       "      <td>26.3</td>\n",
       "      <td>28.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>37.2</td>\n",
       "      <td>91.8</td>\n",
       "      <td>23.7</td>\n",
       "      <td>1.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>82</td>\n",
       "      <td>南京工业大学</td>\n",
       "      <td>江苏</td>\n",
       "      <td>理工</td>\n",
       "      <td>235.4</td>\n",
       "      <td>25.4</td>\n",
       "      <td>14.4</td>\n",
       "      <td>3.3</td>\n",
       "      <td>33.0</td>\n",
       "      <td>102.2</td>\n",
       "      <td>15.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>6.6</td>\n",
       "      <td>12.0</td>\n",
       "      <td>15.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>81</td>\n",
       "      <td>浙江工业大学</td>\n",
       "      <td>浙江</td>\n",
       "      <td>理工</td>\n",
       "      <td>235.5</td>\n",
       "      <td>27.6</td>\n",
       "      <td>18.7</td>\n",
       "      <td>5.7</td>\n",
       "      <td>28.7</td>\n",
       "      <td>109.5</td>\n",
       "      <td>17.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.8</td>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     排名      学校名称  省市 学校类型     总分  办学层次得分  学科水平得分  办学资源得分  师资规模与结构得分  人才培养得分  \\\n",
       "99  100      广州大学  广东   综合  211.1    23.2    16.4     5.0       33.7    95.9   \n",
       "98   99  杭州电子科技大学  浙江   理工  211.6    25.4    12.6     2.7       36.5   103.4   \n",
       "97   98    上海师范大学  上海   师范  212.8    27.3    17.9     3.6       32.1    96.9   \n",
       "96   97      河南大学  河南   综合  212.9    24.2    22.7     3.4       32.5    97.5   \n",
       "95   96    南京邮电大学  江苏   综合  213.9    25.0    12.5     2.4       34.8   101.2   \n",
       "94   95    广东工业大学  广东   理工  214.2    24.2    15.5     3.7       32.6    96.7   \n",
       "93   94      湖北大学  湖北   综合  214.5    26.3    14.7     2.3       35.0   105.8   \n",
       "92   93  南京信息工程大学  江苏   理工  216.6    23.6    16.1     2.4       33.6    97.5   \n",
       "91   92      燕山大学  河北   理工  216.7    26.6    15.2     2.3       34.5   107.2   \n",
       "90   91      长安大学  陕西   理工  218.9    27.2    14.0     3.7       34.1   104.9   \n",
       "89   90      安徽大学  安徽   综合  219.2    25.7    19.0     2.6       29.5   110.8   \n",
       "88   89    上海理工大学  上海   理工  221.4    28.3    16.4     3.6       32.3   105.7   \n",
       "87   88    华南农业大学  广东   农业  227.1    23.2    18.0     4.5       35.0   102.4   \n",
       "86   87      南昌大学  江西   综合  228.4    27.3    21.1     4.1       32.3   105.2   \n",
       "84   85    湖南师范大学  湖南   师范  233.4    27.9    20.6     2.7       35.2   109.8   \n",
       "85   85    首都师范大学  北京   师范  233.4    29.5    22.9     5.2       37.6    99.7   \n",
       "83   84      云南大学  云南   综合  234.1    29.0    22.8     5.0       30.8   111.6   \n",
       "82   83      郑州大学  河南   综合  234.9    26.3    28.3     6.0       37.2    91.8   \n",
       "81   82    南京工业大学  江苏   理工  235.4    25.4    14.4     3.3       33.0   102.2   \n",
       "80   81    浙江工业大学  浙江   理工  235.5    27.6    18.7     5.7       28.7   109.5   \n",
       "\n",
       "    科学研究得分  社会服务得分  高端人才得分  重大项目与成果得分  国际竞争力得分  \n",
       "99    14.4     0.6     2.0        5.2     14.8  \n",
       "98    12.0     2.5     1.5        2.6     12.3  \n",
       "97    14.0     0.5     2.0        6.8     11.8  \n",
       "96    15.7     2.1     1.3        4.2      9.2  \n",
       "95    12.4     6.5     1.6        4.6     13.0  \n",
       "94    13.8     3.2     3.1        5.3     16.2  \n",
       "93    10.5     2.9     1.2        3.7     12.1  \n",
       "92    15.1     4.7     2.1        3.6     18.0  \n",
       "91    12.6     2.8     2.5        4.8      8.2  \n",
       "90    12.1    12.4     1.1        1.1      8.2  \n",
       "89    15.5     1.4     0.8        3.4     10.5  \n",
       "88    12.6     9.8     1.3        1.5      9.9  \n",
       "87    14.8     2.2     4.0       10.3     12.7  \n",
       "86    15.6     0.5     2.5        7.8     12.0  \n",
       "84    15.7     0.5     1.9        7.7     11.4  \n",
       "85    14.7     0.3     3.3        8.3     11.8  \n",
       "83    14.0     0.4     3.0        6.4     11.2  \n",
       "82    23.7     1.7     2.0        4.9     13.0  \n",
       "81    15.0     8.6     6.6       12.0     15.1  \n",
       "80    17.8     5.6     2.5        5.8     13.5  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by='总分',ascending=True).head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面是 `sort_values()` 方法各个参数的详细说明表格，可以帮助你快速掌握和使用。\n",
    "\n",
    "| 参数名 | 数据类型 | 默认值 | 描述与用途 | 常用场景/示例 |\n",
    "| :--- | :--- | :--- | :--- | :--- |\n",
    "| **`by`** | str 或 list of str | (无默认值，**必需**) | 指定根据哪个或哪些标签（列名或索引名）进行排序。如果 `axis=0`，则按列名排序；如果 `axis=1`，则按索引名排序。 | 单列排序：`by='Age'`<br>多列排序：`by=['Age', 'Score']` |\n",
    "| **`axis`** | {0, 1, 'index', 'columns'} | 0 ('index') | 指定沿着哪个轴排序。`0` 或 `'index'` 表示按列的值对**行**进行排序；`1` 或 `'columns'` 表示按行的值对**列**进行排序。 | 绝大多数情况是按列的值排序行，故使用默认值 `0` 即可。 |\n",
    "| **`ascending`** | bool 或 list of bool | True | 指定排序方向。`True` 为升序，`False` 为降序。当按多列排序时，可传入一个布尔值列表，分别指定每一列的排序方向。 | 单列降序：`ascending=False`<br>多列不同向：`ascending=[True, False]` |\n",
    "| **`inplace`** | bool | False | 决定操作是否在原DataFrame上直接修改。`False` 时返回排序后的新DataFrame，原数据不变；`True` 时直接在原数据上排序，不返回新对象。 | 需保留原始数据：`inplace=False`<br>确认修改原数据：`inplace=True` |\n",
    "| **`kind`** | {'quicksort', 'mergesort', 'heapsort', 'stable'} | 'quicksort' | 选择排序算法。对于DataFrame，此选项通常仅在按单列排序时应用。`'mergesort'` 和 `'stable'` 是稳定的排序算法。 | 一般情况默认的 `'quicksort'` 效率高。需要**稳定排序**（相等元素顺序不变）时选 `'mergesort'` 或 `'stable'`。 |\n",
    "| **`na_position`** | {'first', 'last'} | 'last' | 控制缺失值（NaN）在排序结果中的位置。`'first'` 将缺失值放在开头，`'last'` 则放在末尾。 | 希望NaN排在前面：`na_position='first'`<br>希望NaN排在后面：`na_position='last'`（默认） |\n",
    "| **`ignore_index`** | bool | False | 排序后是否重置行索引。`True` 会忽略原来的索引，生成新的从0开始的连续整数索引。 | 排序后索引混乱，希望它变整洁：`ignore_index=True` |\n",
    "| **`key`** | callable | None | 在排序前，对 `by` 指定的列施加一个函数进行转换，排序将基于转换后的结果。类似于内置函数 `sorted()` 的 `key` 参数，但此函数应能处理Series。 | 忽略大小写排序：`key=lambda x: x.str.lower()`<br>按字符串长度排序：`key=lambda x: x.str.len()` |\n",
    "\n",
    "**多列排序是核心功能**：当第一排序字段出现相同值时，`by` 参数中后续列的作用就会体现。你可以通过 `ascending` 列表为每一列精确指定排序方向，例如 `df.sort_values(by=['部门', '薪资'], ascending=[True, False])` 会先按部门升序，部门相同的再按薪资降序排列。\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5 - 数据排序\n",
    "\n",
    "将数据按照 高端人才得分 降序排序，展示前 10 位"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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         "name": "index",
         "rawType": "int64",
         "type": "integer"
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         "type": "integer"
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         "name": "学校名称",
         "rawType": "object",
         "type": "string"
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        {
         "name": "省市",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "学校类型",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "总分",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "办学层次得分",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "学科水平得分",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "办学资源得分",
         "rawType": "float64",
         "type": "float"
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        {
         "name": "师资规模与结构得分",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "人才培养得分",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "科学研究得分",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "社会服务得分",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "高端人才得分",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "重大项目与成果得分",
         "rawType": "float64",
         "type": "float"
        },
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         "name": "国际竞争力得分",
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         "42.9",
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        [
         "4",
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         "南京大学",
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         "566.1",
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         "46",
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         "33.8",
         "32.6"
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         "8",
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       ],
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      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>排名</th>\n",
       "      <th>学校名称</th>\n",
       "      <th>省市</th>\n",
       "      <th>学校类型</th>\n",
       "      <th>总分</th>\n",
       "      <th>办学层次得分</th>\n",
       "      <th>学科水平得分</th>\n",
       "      <th>办学资源得分</th>\n",
       "      <th>师资规模与结构得分</th>\n",
       "      <th>人才培养得分</th>\n",
       "      <th>科学研究得分</th>\n",
       "      <th>社会服务得分</th>\n",
       "      <th>高端人才得分</th>\n",
       "      <th>重大项目与成果得分</th>\n",
       "      <th>国际竞争力得分</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>清华大学</td>\n",
       "      <td>北京</td>\n",
       "      <td>综合</td>\n",
       "      <td>852.5</td>\n",
       "      <td>38.2</td>\n",
       "      <td>72.4</td>\n",
       "      <td>39.6</td>\n",
       "      <td>48.4</td>\n",
       "      <td>256.8</td>\n",
       "      <td>69.1</td>\n",
       "      <td>40.6</td>\n",
       "      <td>76.5</td>\n",
       "      <td>131.0</td>\n",
       "      <td>79.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>北京大学</td>\n",
       "      <td>北京</td>\n",
       "      <td>综合</td>\n",
       "      <td>746.7</td>\n",
       "      <td>36.1</td>\n",
       "      <td>73.1</td>\n",
       "      <td>24.6</td>\n",
       "      <td>49.2</td>\n",
       "      <td>237.6</td>\n",
       "      <td>71.0</td>\n",
       "      <td>16.2</td>\n",
       "      <td>71.9</td>\n",
       "      <td>105.8</td>\n",
       "      <td>61.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>中国科学技术大学</td>\n",
       "      <td>安徽</td>\n",
       "      <td>理工</td>\n",
       "      <td>526.4</td>\n",
       "      <td>40.0</td>\n",
       "      <td>39.1</td>\n",
       "      <td>10.6</td>\n",
       "      <td>45.9</td>\n",
       "      <td>191.5</td>\n",
       "      <td>52.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>55.1</td>\n",
       "      <td>49.2</td>\n",
       "      <td>42.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>浙江大学</td>\n",
       "      <td>浙江</td>\n",
       "      <td>综合</td>\n",
       "      <td>649.2</td>\n",
       "      <td>33.9</td>\n",
       "      <td>65.3</td>\n",
       "      <td>20.1</td>\n",
       "      <td>48.3</td>\n",
       "      <td>215.3</td>\n",
       "      <td>68.6</td>\n",
       "      <td>23.9</td>\n",
       "      <td>49.1</td>\n",
       "      <td>81.7</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>上海交通大学</td>\n",
       "      <td>上海</td>\n",
       "      <td>综合</td>\n",
       "      <td>625.9</td>\n",
       "      <td>35.4</td>\n",
       "      <td>53.6</td>\n",
       "      <td>22.1</td>\n",
       "      <td>43.8</td>\n",
       "      <td>192.8</td>\n",
       "      <td>81.2</td>\n",
       "      <td>18.1</td>\n",
       "      <td>45.8</td>\n",
       "      <td>93.0</td>\n",
       "      <td>40.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>复旦大学</td>\n",
       "      <td>上海</td>\n",
       "      <td>综合</td>\n",
       "      <td>556.7</td>\n",
       "      <td>36.6</td>\n",
       "      <td>48.4</td>\n",
       "      <td>14.9</td>\n",
       "      <td>46.3</td>\n",
       "      <td>198.5</td>\n",
       "      <td>65.7</td>\n",
       "      <td>6.5</td>\n",
       "      <td>42.9</td>\n",
       "      <td>62.0</td>\n",
       "      <td>34.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>南京大学</td>\n",
       "      <td>江苏</td>\n",
       "      <td>综合</td>\n",
       "      <td>566.1</td>\n",
       "      <td>35.1</td>\n",
       "      <td>47.8</td>\n",
       "      <td>10.3</td>\n",
       "      <td>47.4</td>\n",
       "      <td>218.6</td>\n",
       "      <td>59.6</td>\n",
       "      <td>5.3</td>\n",
       "      <td>42.0</td>\n",
       "      <td>71.2</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>46</td>\n",
       "      <td>南方科技大学</td>\n",
       "      <td>广东</td>\n",
       "      <td>综合</td>\n",
       "      <td>289.0</td>\n",
       "      <td>26.7</td>\n",
       "      <td>7.1</td>\n",
       "      <td>16.9</td>\n",
       "      <td>41.9</td>\n",
       "      <td>105.0</td>\n",
       "      <td>26.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>38.9</td>\n",
       "      <td>7.1</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>中山大学</td>\n",
       "      <td>广东</td>\n",
       "      <td>综合</td>\n",
       "      <td>457.2</td>\n",
       "      <td>30.3</td>\n",
       "      <td>47.1</td>\n",
       "      <td>13.7</td>\n",
       "      <td>46.8</td>\n",
       "      <td>154.4</td>\n",
       "      <td>65.9</td>\n",
       "      <td>5.6</td>\n",
       "      <td>27.1</td>\n",
       "      <td>33.8</td>\n",
       "      <td>32.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>华中科技大学</td>\n",
       "      <td>湖北</td>\n",
       "      <td>综合</td>\n",
       "      <td>497.7</td>\n",
       "      <td>31.9</td>\n",
       "      <td>45.2</td>\n",
       "      <td>11.3</td>\n",
       "      <td>44.2</td>\n",
       "      <td>182.8</td>\n",
       "      <td>58.3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>25.5</td>\n",
       "      <td>44.9</td>\n",
       "      <td>31.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    排名      学校名称  省市 学校类型     总分  办学层次得分  学科水平得分  办学资源得分  师资规模与结构得分  人才培养得分  \\\n",
       "0    1      清华大学  北京   综合  852.5    38.2    72.4    39.6       48.4   256.8   \n",
       "1    2      北京大学  北京   综合  746.7    36.1    73.1    24.6       49.2   237.6   \n",
       "6    7  中国科学技术大学  安徽   理工  526.4    40.0    39.1    10.6       45.9   191.5   \n",
       "2    3      浙江大学  浙江   综合  649.2    33.9    65.3    20.1       48.3   215.3   \n",
       "3    4    上海交通大学  上海   综合  625.9    35.4    53.6    22.1       43.8   192.8   \n",
       "5    6      复旦大学  上海   综合  556.7    36.6    48.4    14.9       46.3   198.5   \n",
       "4    5      南京大学  江苏   综合  566.1    35.1    47.8    10.3       47.4   218.6   \n",
       "45  46    南方科技大学  广东   综合  289.0    26.7     7.1    16.9       41.9   105.0   \n",
       "9   10      中山大学  广东   综合  457.2    30.3    47.1    13.7       46.8   154.4   \n",
       "7    8    华中科技大学  湖北   综合  497.7    31.9    45.2    11.3       44.2   182.8   \n",
       "\n",
       "    科学研究得分  社会服务得分  高端人才得分  重大项目与成果得分  国际竞争力得分  \n",
       "0     69.1    40.6    76.5      131.0     79.9  \n",
       "1     71.0    16.2    71.9      105.8     61.2  \n",
       "6     52.6     0.2    55.1       49.2     42.2  \n",
       "2     68.6    23.9    49.1       81.7     43.0  \n",
       "3     81.2    18.1    45.8       93.0     40.1  \n",
       "5     65.7     6.5    42.9       62.0     34.8  \n",
       "4     59.6     5.3    42.0       71.2     29.0  \n",
       "45    26.4     1.0    38.9        7.1     18.0  \n",
       "9     65.9     5.6    27.1       33.8     32.6  \n",
       "7     58.3    22.0    25.5       44.9     31.8  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by='高端人才得分',ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6 - 分列排名\n",
    "\n",
    "<br>\n",
    "\n",
    "查看各项得分最高的学校名称"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "index",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "分数类型",
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         "type": "string"
        },
        {
         "name": "分数",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "学校名称",
         "rawType": "object",
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         "852.5",
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         "中国科学技术大学"
        ],
        [
         "2",
         "学科水平得分",
         "73.1",
         "北京大学"
        ],
        [
         "3",
         "办学资源得分",
         "39.6",
         "清华大学"
        ],
        [
         "4",
         "师资规模与结构得分",
         "49.2",
         "北京大学"
        ],
        [
         "5",
         "人才培养得分",
         "256.8",
         "清华大学"
        ],
        [
         "6",
         "科学研究得分",
         "81.2",
         "上海交通大学"
        ],
        [
         "7",
         "社会服务得分",
         "40.6",
         "清华大学"
        ],
        [
         "8",
         "高端人才得分",
         "76.5",
         "清华大学"
        ],
        [
         "9",
         "重大项目与成果得分",
         "131.0",
         "清华大学"
        ],
        [
         "10",
         "国际竞争力得分",
         "79.9",
         "清华大学"
        ]
       ],
       "shape": {
        "columns": 3,
        "rows": 11
       }
      },
      "text/html": [
       "<div>\n",
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       "      <th></th>\n",
       "      <th>分数类型</th>\n",
       "      <th>分数</th>\n",
       "      <th>学校名称</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>总分</td>\n",
       "      <td>852.5</td>\n",
       "      <td>清华大学</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>办学层次得分</td>\n",
       "      <td>40.0</td>\n",
       "      <td>中国科学技术大学</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>学科水平得分</td>\n",
       "      <td>73.1</td>\n",
       "      <td>北京大学</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>办学资源得分</td>\n",
       "      <td>39.6</td>\n",
       "      <td>清华大学</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>师资规模与结构得分</td>\n",
       "      <td>49.2</td>\n",
       "      <td>北京大学</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>人才培养得分</td>\n",
       "      <td>256.8</td>\n",
       "      <td>清华大学</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>科学研究得分</td>\n",
       "      <td>81.2</td>\n",
       "      <td>上海交通大学</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>社会服务得分</td>\n",
       "      <td>40.6</td>\n",
       "      <td>清华大学</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>高端人才得分</td>\n",
       "      <td>76.5</td>\n",
       "      <td>清华大学</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>重大项目与成果得分</td>\n",
       "      <td>131.0</td>\n",
       "      <td>清华大学</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>国际竞争力得分</td>\n",
       "      <td>79.9</td>\n",
       "      <td>清华大学</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         分数类型     分数      学校名称\n",
       "0          总分  852.5      清华大学\n",
       "1      办学层次得分   40.0  中国科学技术大学\n",
       "2      学科水平得分   73.1      北京大学\n",
       "3      办学资源得分   39.6      清华大学\n",
       "4   师资规模与结构得分   49.2      北京大学\n",
       "5      人才培养得分  256.8      清华大学\n",
       "6      科学研究得分   81.2    上海交通大学\n",
       "7      社会服务得分   40.6      清华大学\n",
       "8      高端人才得分   76.5      清华大学\n",
       "9   重大项目与成果得分  131.0      清华大学\n",
       "10    国际竞争力得分   79.9      清华大学"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df = pd.read_excel(\"2020年中国大学排名.xlsx\")\n",
    "df.set_index(keys='学校名称',inplace=True)\n",
    "score_cols=df.select_dtypes(include='float').columns.to_list()\n",
    "pd.DataFrame({\n",
    "    '分数类型':score_cols,\n",
    "    '分数':[df[col].max() for col in score_cols],\n",
    "    '学校名称':[df[col].idxmax() for col in score_cols]\n",
    "})\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7 - 统计信息｜均值\n",
    "\n",
    "计算总分列的均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "index",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "0",
         "rawType": "float64",
         "type": "float"
        }
       ],
       "ref": "10345b8d-4c6c-4c9a-ac33-39979f72f6e5",
       "rows": [
        [
         "排名",
         "50.49"
        ],
        [
         "总分",
         "322.5"
        ],
        [
         "办学层次得分",
         "29.691999999999997"
        ],
        [
         "学科水平得分",
         "27.489999999999995"
        ],
        [
         "办学资源得分",
         "7.187"
        ],
        [
         "师资规模与结构得分",
         "39.028999999999996"
        ],
        [
         "人才培养得分",
         "134.414"
        ],
        [
         "科学研究得分",
         "26.874000000000002"
        ],
        [
         "社会服务得分",
         "7.472"
        ],
        [
         "高端人才得分",
         "11.175999999999998"
        ],
        [
         "重大项目与成果得分",
         "20.372"
        ],
        [
         "国际竞争力得分",
         "18.804999999999996"
        ]
       ],
       "shape": {
        "columns": 1,
        "rows": 12
       }
      },
      "text/plain": [
       "排名            50.490\n",
       "总分           322.500\n",
       "办学层次得分        29.692\n",
       "学科水平得分        27.490\n",
       "办学资源得分         7.187\n",
       "师资规模与结构得分     39.029\n",
       "人才培养得分       134.414\n",
       "科学研究得分        26.874\n",
       "社会服务得分         7.472\n",
       "高端人才得分        11.176\n",
       "重大项目与成果得分     20.372\n",
       "国际竞争力得分       18.805\n",
       "dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean(numeric_only=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8 - 统计信息｜中位数\n",
    "\n",
    "<br>\n",
    "\n",
    "计算总分列的中位数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "index",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "0",
         "rawType": "float64",
         "type": "float"
        }
       ],
       "ref": "e0c36118-f125-4018-b34a-e6d1749bfdfe",
       "rows": [
        [
         "排名",
         "50.5"
        ],
        [
         "总分",
         "279.65"
        ],
        [
         "办学层次得分",
         "29.4"
        ],
        [
         "学科水平得分",
         "24.0"
        ],
        [
         "办学资源得分",
         "5.25"
        ],
        [
         "师资规模与结构得分",
         "37.95"
        ],
        [
         "人才培养得分",
         "122.8"
        ],
        [
         "科学研究得分",
         "21.85"
        ],
        [
         "社会服务得分",
         "5.2"
        ],
        [
         "高端人才得分",
         "6.15"
        ],
        [
         "重大项目与成果得分",
         "13.75"
        ],
        [
         "国际竞争力得分",
         "15.6"
        ]
       ],
       "shape": {
        "columns": 1,
        "rows": 12
       }
      },
      "text/plain": [
       "排名            50.50\n",
       "总分           279.65\n",
       "办学层次得分        29.40\n",
       "学科水平得分        24.00\n",
       "办学资源得分         5.25\n",
       "师资规模与结构得分     37.95\n",
       "人才培养得分       122.80\n",
       "科学研究得分        21.85\n",
       "社会服务得分         5.20\n",
       "高端人才得分         6.15\n",
       "重大项目与成果得分     13.75\n",
       "国际竞争力得分       15.60\n",
       "dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.median(numeric_only=True).round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9 - 统计信息｜众数\n",
    "\n",
    "\n",
    "计算总分列的众数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
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        {
         "name": "index",
         "rawType": "int64",
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        {
         "name": "排名",
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         "type": "float"
        },
        {
         "name": "省市",
         "rawType": "object",
         "type": "unknown"
        },
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         "name": "学校类型",
         "rawType": "object",
         "type": "unknown"
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         "name": "总分",
         "rawType": "float64",
         "type": "float"
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         "name": "办学层次得分",
         "rawType": "float64",
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         "name": "学科水平得分",
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         "name": "社会服务得分",
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         "type": "float"
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         "name": "高端人才得分",
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         "name": "国际竞争力得分",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     排名   省市 学校类型     总分  办学层次得分  学科水平得分  办学资源得分  师资规模与结构得分  人才培养得分  科学研究得分  \\\n",
       "0  85.0   北京   理工  233.4    32.5    16.4     5.0       37.5    97.5    12.1   \n",
       "1   NaN  NaN   综合    NaN     NaN    22.7     NaN        NaN     NaN    12.6   \n",
       "2   NaN  NaN  NaN    NaN     NaN     NaN     NaN        NaN     NaN    14.0   \n",
       "3   NaN  NaN  NaN    NaN     NaN     NaN     NaN        NaN     NaN    15.7   \n",
       "4   NaN  NaN  NaN    NaN     NaN     NaN     NaN        NaN     NaN    19.2   \n",
       "5   NaN  NaN  NaN    NaN     NaN     NaN     NaN        NaN     NaN    20.1   \n",
       "6   NaN  NaN  NaN    NaN     NaN     NaN     NaN        NaN     NaN    25.1   \n",
       "7   NaN  NaN  NaN    NaN     NaN     NaN     NaN        NaN     NaN    26.9   \n",
       "8   NaN  NaN  NaN    NaN     NaN     NaN     NaN        NaN     NaN    35.7   \n",
       "\n",
       "   社会服务得分  高端人才得分  重大项目与成果得分  国际竞争力得分  \n",
       "0     0.5     2.0        8.0     11.8  \n",
       "1     NaN     2.5        NaN      NaN  \n",
       "2     NaN     NaN        NaN      NaN  \n",
       "3     NaN     NaN        NaN      NaN  \n",
       "4     NaN     NaN        NaN      NaN  \n",
       "5     NaN     NaN        NaN      NaN  \n",
       "6     NaN     NaN        NaN      NaN  \n",
       "7     NaN     NaN        NaN      NaN  \n",
       "8     NaN     NaN        NaN      NaN  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mode()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10 -统计信息｜部分\n",
    "\n",
    "计算 总分、高端人才得分、办学层次得分的最大最小值、中位数、均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "index",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "总分",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "高端人才得分",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "办学层次得分",
         "rawType": "float64",
         "type": "float"
        }
       ],
       "ref": "b73bde56-ed52-41f7-bc63-fbcf617f9418",
       "rows": [
        [
         "max",
         "852.5",
         "76.5",
         "40.0"
        ],
        [
         "min",
         "211.1",
         "0.8",
         "23.2"
        ],
        [
         "median",
         "279.65",
         "6.15",
         "29.4"
        ],
        [
         "mean",
         "322.5",
         "11.18",
         "29.69"
        ]
       ],
       "shape": {
        "columns": 3,
        "rows": 4
       }
      },
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>总分</th>\n",
       "      <th>高端人才得分</th>\n",
       "      <th>办学层次得分</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>852.50</td>\n",
       "      <td>76.50</td>\n",
       "      <td>40.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>211.10</td>\n",
       "      <td>0.80</td>\n",
       "      <td>23.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median</th>\n",
       "      <td>279.65</td>\n",
       "      <td>6.15</td>\n",
       "      <td>29.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>322.50</td>\n",
       "      <td>11.18</td>\n",
       "      <td>29.69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            总分  高端人才得分  办学层次得分\n",
       "max     852.50   76.50   40.00\n",
       "min     211.10    0.80   23.20\n",
       "median  279.65    6.15   29.40\n",
       "mean    322.50   11.18   29.69"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.agg({\n",
    "    '总分':['max','min','median','mean'],\n",
    "    '高端人才得分':['max','min','median','mean'],\n",
    "    '办学层次得分':['max','min','median','mean']\n",
    "}).round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 11 - 统计信息｜完整\n",
    "\n",
    "<br>\n",
    "\n",
    "查看数值型数据的统计信息（均值、分位数等），并保留两位小数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "index",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "排名",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "总分",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "办学层次得分",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "学科水平得分",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "办学资源得分",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "师资规模与结构得分",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "人才培养得分",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "科学研究得分",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "社会服务得分",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "高端人才得分",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "重大项目与成果得分",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "国际竞争力得分",
         "rawType": "float64",
         "type": "float"
        }
       ],
       "ref": "f1e80181-f713-40ff-8fa8-a1b3f1990e8e",
       "rows": [
        [
         "count",
         "100.0",
         "100.0",
         "100.0",
         "100.0",
         "100.0",
         "100.0",
         "100.0",
         "100.0",
         "100.0",
         "100.0",
         "100.0",
         "100.0"
        ],
        [
         "mean",
         "50.49",
         "322.5",
         "29.69",
         "27.49",
         "7.19",
         "39.03",
         "134.41",
         "26.87",
         "7.47",
         "11.18",
         "20.37",
         "18.8"
        ],
        [
         "std",
         "29.0",
         "118.36",
         "3.31",
         "12.42",
         "5.51",
         "4.75",
         "33.82",
         "15.61",
         "7.99",
         "14.14",
         "22.02",
         "10.56"
        ],
        [
         "min",
         "1.0",
         "211.1",
         "23.2",
         "6.8",
         "2.3",
         "28.7",
         "91.8",
         "9.7",
         "0.0",
         "0.8",
         "1.1",
         "8.2"
        ],
        [
         "25%",
         "25.75",
         "244.18",
         "27.3",
         "19.65",
         "3.98",
         "35.98",
         "109.08",
         "15.68",
         "2.2",
         "3.08",
         "7.48",
         "12.7"
        ],
        [
         "50%",
         "50.5",
         "279.65",
         "29.4",
         "24.0",
         "5.25",
         "37.95",
         "122.8",
         "21.85",
         "5.2",
         "6.15",
         "13.75",
         "15.6"
        ],
        [
         "75%",
         "75.25",
         "378.8",
         "31.9",
         "33.92",
         "9.02",
         "43.2",
         "154.75",
         "32.72",
         "9.8",
         "12.35",
         "24.78",
         "21.0"
        ],
        [
         "max",
         "100.0",
         "852.5",
         "40.0",
         "73.1",
         "39.6",
         "49.2",
         "256.8",
         "81.2",
         "40.6",
         "76.5",
         "131.0",
         "79.9"
        ]
       ],
       "shape": {
        "columns": 12,
        "rows": 8
       }
      },
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>排名</th>\n",
       "      <th>总分</th>\n",
       "      <th>办学层次得分</th>\n",
       "      <th>学科水平得分</th>\n",
       "      <th>办学资源得分</th>\n",
       "      <th>师资规模与结构得分</th>\n",
       "      <th>人才培养得分</th>\n",
       "      <th>科学研究得分</th>\n",
       "      <th>社会服务得分</th>\n",
       "      <th>高端人才得分</th>\n",
       "      <th>重大项目与成果得分</th>\n",
       "      <th>国际竞争力得分</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>100.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>50.49</td>\n",
       "      <td>322.50</td>\n",
       "      <td>29.69</td>\n",
       "      <td>27.49</td>\n",
       "      <td>7.19</td>\n",
       "      <td>39.03</td>\n",
       "      <td>134.41</td>\n",
       "      <td>26.87</td>\n",
       "      <td>7.47</td>\n",
       "      <td>11.18</td>\n",
       "      <td>20.37</td>\n",
       "      <td>18.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>29.00</td>\n",
       "      <td>118.36</td>\n",
       "      <td>3.31</td>\n",
       "      <td>12.42</td>\n",
       "      <td>5.51</td>\n",
       "      <td>4.75</td>\n",
       "      <td>33.82</td>\n",
       "      <td>15.61</td>\n",
       "      <td>7.99</td>\n",
       "      <td>14.14</td>\n",
       "      <td>22.02</td>\n",
       "      <td>10.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.00</td>\n",
       "      <td>211.10</td>\n",
       "      <td>23.20</td>\n",
       "      <td>6.80</td>\n",
       "      <td>2.30</td>\n",
       "      <td>28.70</td>\n",
       "      <td>91.80</td>\n",
       "      <td>9.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1.10</td>\n",
       "      <td>8.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>25.75</td>\n",
       "      <td>244.18</td>\n",
       "      <td>27.30</td>\n",
       "      <td>19.65</td>\n",
       "      <td>3.98</td>\n",
       "      <td>35.98</td>\n",
       "      <td>109.08</td>\n",
       "      <td>15.68</td>\n",
       "      <td>2.20</td>\n",
       "      <td>3.08</td>\n",
       "      <td>7.48</td>\n",
       "      <td>12.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>50.50</td>\n",
       "      <td>279.65</td>\n",
       "      <td>29.40</td>\n",
       "      <td>24.00</td>\n",
       "      <td>5.25</td>\n",
       "      <td>37.95</td>\n",
       "      <td>122.80</td>\n",
       "      <td>21.85</td>\n",
       "      <td>5.20</td>\n",
       "      <td>6.15</td>\n",
       "      <td>13.75</td>\n",
       "      <td>15.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>75.25</td>\n",
       "      <td>378.80</td>\n",
       "      <td>31.90</td>\n",
       "      <td>33.92</td>\n",
       "      <td>9.02</td>\n",
       "      <td>43.20</td>\n",
       "      <td>154.75</td>\n",
       "      <td>32.72</td>\n",
       "      <td>9.80</td>\n",
       "      <td>12.35</td>\n",
       "      <td>24.78</td>\n",
       "      <td>21.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.00</td>\n",
       "      <td>852.50</td>\n",
       "      <td>40.00</td>\n",
       "      <td>73.10</td>\n",
       "      <td>39.60</td>\n",
       "      <td>49.20</td>\n",
       "      <td>256.80</td>\n",
       "      <td>81.20</td>\n",
       "      <td>40.60</td>\n",
       "      <td>76.50</td>\n",
       "      <td>131.00</td>\n",
       "      <td>79.90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           排名      总分  办学层次得分  学科水平得分  办学资源得分  师资规模与结构得分  人才培养得分  科学研究得分  \\\n",
       "count  100.00  100.00  100.00  100.00  100.00     100.00  100.00  100.00   \n",
       "mean    50.49  322.50   29.69   27.49    7.19      39.03  134.41   26.87   \n",
       "std     29.00  118.36    3.31   12.42    5.51       4.75   33.82   15.61   \n",
       "min      1.00  211.10   23.20    6.80    2.30      28.70   91.80    9.70   \n",
       "25%     25.75  244.18   27.30   19.65    3.98      35.98  109.08   15.68   \n",
       "50%     50.50  279.65   29.40   24.00    5.25      37.95  122.80   21.85   \n",
       "75%     75.25  378.80   31.90   33.92    9.02      43.20  154.75   32.72   \n",
       "max    100.00  852.50   40.00   73.10   39.60      49.20  256.80   81.20   \n",
       "\n",
       "       社会服务得分  高端人才得分  重大项目与成果得分  国际竞争力得分  \n",
       "count  100.00  100.00     100.00   100.00  \n",
       "mean     7.47   11.18      20.37    18.80  \n",
       "std      7.99   14.14      22.02    10.56  \n",
       "min      0.00    0.80       1.10     8.20  \n",
       "25%      2.20    3.08       7.48    12.70  \n",
       "50%      5.20    6.15      13.75    15.60  \n",
       "75%      9.80   12.35      24.78    21.00  \n",
       "max     40.60   76.50     131.00    79.90  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 12 - 统计信息｜分组\n",
    "\n",
    "计算各省市总分均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "省市",
         "rawType": "object",
         "type": "string"
        },
        {
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      ],
      "text/plain": [
       "         总分\n",
       "省市         \n",
       "天津   396.20\n",
       "黑龙江  364.80\n",
       "北京   362.83\n",
       "上海   350.51\n",
       "四川   349.27\n",
       "辽宁   339.55\n",
       "浙江   335.15\n",
       "湖北   335.09\n",
       "安徽   328.70\n",
       "吉林   326.00\n",
       "湖南   320.10\n",
       "福建   313.55\n",
       "山东   304.63\n",
       "甘肃   300.40\n",
       "江苏   298.39\n",
       "陕西   297.69\n",
       "重庆   289.20\n",
       "广东   286.01\n",
       "云南   234.10\n",
       "江西   228.40\n",
       "河南   223.90\n",
       "河北   216.70"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(by='省市').agg({\n",
    "    '总分':'mean'\n",
    "}).sort_values(by='总分',ascending=False).round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 13 - 统计信息｜相关系数\n",
    "\n",
    "<br>\n",
    "\n",
    "也就是相关系数矩阵，也就是每两列之间的相关性系数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
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       "      <td>0.91</td>\n",
       "      <td>0.88</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             排名    总分  办学层次得分  学科水平得分  办学资源得分  师资规模与结构得分  人才培养得分  科学研究得分  \\\n",
       "排名         1.00 -0.85   -0.82   -0.81   -0.63      -0.89   -0.89   -0.84   \n",
       "总分        -0.85  1.00    0.79    0.93    0.84       0.83    0.96    0.94   \n",
       "办学层次得分    -0.82  0.79    1.00    0.70    0.65       0.76    0.80    0.71   \n",
       "学科水平得分    -0.81  0.93    0.70    1.00    0.72       0.78    0.89    0.93   \n",
       "办学资源得分    -0.63  0.84    0.65    0.72    1.00       0.70    0.73    0.75   \n",
       "师资规模与结构得分 -0.89  0.83    0.76    0.78    0.70       1.00    0.83    0.81   \n",
       "人才培养得分    -0.89  0.96    0.80    0.89    0.73       0.83    1.00    0.89   \n",
       "科学研究得分    -0.84  0.94    0.71    0.93    0.75       0.81    0.89    1.00   \n",
       "社会服务得分    -0.51  0.59    0.42    0.48    0.46       0.43    0.57    0.48   \n",
       "高端人才得分    -0.70  0.92    0.70    0.79    0.86       0.72    0.83    0.86   \n",
       "重大项目与成果得分 -0.73  0.95    0.74    0.90    0.81       0.71    0.87    0.88   \n",
       "国际竞争力得分   -0.69  0.92    0.67    0.83    0.85       0.71    0.83    0.83   \n",
       "\n",
       "           社会服务得分  高端人才得分  重大项目与成果得分  国际竞争力得分  \n",
       "排名          -0.51   -0.70      -0.73    -0.69  \n",
       "总分           0.59    0.92       0.95     0.92  \n",
       "办学层次得分       0.42    0.70       0.74     0.67  \n",
       "学科水平得分       0.48    0.79       0.90     0.83  \n",
       "办学资源得分       0.46    0.86       0.81     0.85  \n",
       "师资规模与结构得分    0.43    0.72       0.71     0.71  \n",
       "人才培养得分       0.57    0.83       0.87     0.83  \n",
       "科学研究得分       0.48    0.86       0.88     0.83  \n",
       "社会服务得分       1.00    0.46       0.50     0.53  \n",
       "高端人才得分       0.46    1.00       0.90     0.91  \n",
       "重大项目与成果得分    0.50    0.90       1.00     0.88  \n",
       "国际竞争力得分      0.53    0.91       0.88     1.00  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr(numeric_only=True).round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 14 - 相关系数｜热力图\n",
    "\n",
    "<br>\n",
    "\n",
    "将上一题的相关性系数矩阵制作为热力图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 设置中文字体和解决负号显示问题\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  #  Windows 系统常用黑体\n",
    "# plt.rcParams['font.sans-serif'] = ['PingFang SC']  #  macOS 系统常用苹方\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 解决负号显示为方块的问题\n",
    "\n",
    "# 假设 corr_matrix 是你通过 df.corr() 得到的相关性系数矩阵\n",
    "corr_matrix = df.corr(numeric_only=True).round(2)\n",
    "\n",
    "# 绘制热力图\n",
    "plt.figure(figsize=(10, 8))  # 可选：设置图形大小\n",
    "sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', center=0, square=True, fmt='.2f')\n",
    "plt.title('相关性系数热力图')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 15 - 统计信息｜频率\n",
    "\n",
    "计算各省市出现的次数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     省市  大学数量\n",
      "0    上海    10\n",
      "1    云南     1\n",
      "2    北京    18\n",
      "3    吉林     2\n",
      "4    四川     3\n",
      "5    天津     2\n",
      "6    安徽     3\n",
      "7    山东     3\n",
      "8    广东     9\n",
      "9    江苏    15\n",
      "10   江西     1\n",
      "11   河北     1\n",
      "12   河南     2\n",
      "13   浙江     4\n",
      "14   湖北     7\n",
      "15   湖南     3\n",
      "16   甘肃     1\n",
      "17   福建     2\n",
      "18   辽宁     2\n",
      "19   重庆     2\n",
      "20   陕西     7\n",
      "21  黑龙江     2\n"
     ]
    }
   ],
   "source": [
    "# 1. 按'省市'列分组，并统计每组的大小（即大学数量）\n",
    "university_counts = df.groupby('省市').size()\n",
    "\n",
    "# 2. （可选）将Series结果转换为DataFrame，使结果更规整\n",
    "university_counts_df = university_counts.reset_index(name='大学数量')\n",
    "\n",
    "# 3. 查看结果\n",
    "print(university_counts_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "微信搜索公众号「早起Python」，关注后可以获得更多资源！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 16 - 统计信息｜热力地图\n",
    "\n",
    "结合 `pyecharts` 将各省市高校上榜数量进行地图可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 参考脚本q16_map.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 17 - 统计信息｜直方图\n",
    "\n",
    "绘制总分的直方图、密度估计图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 参考脚本q17_plot.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 个 pandas EDA 插件"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在 pandas 之外，还有两个插件可以快速实现 EDA\n",
    "\n",
    "下面不作为习题，仅供介绍，感兴趣可以进一步搜索了解\n",
    "\n",
    "执行全部代码即可获得 EDA 报告！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 18 - pandas_profiling\n",
    "\n",
    "<br>\n",
    "\n",
    "如果没有提前安装 `pandas_profiling` 的话，需要提前 `pip` 进行安装"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! pip install pandas_profiling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas_profiling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "pandas_profiling.ProfileReport(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 19 - sweetviz\n",
    "\n",
    "如果没有提前安装 `sweetviz` 的话，需要提前 `pip` 进行安装"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! pip install sweetviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sweetviz as sv\n",
    "\n",
    "report = sv.analyze(df)\n",
    "report.show_html()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "执行完上面的代码后，当前目录下会出现一个html文件，打开即可看到相关 EDA 报告"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
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